Open Issues Need Help
View All on GitHubAI Summary: This issue proposes a highly ambitious integration of Deep Tree Echo's Membrane Computing architecture and Echo-Self AI Evolution Engine with the Aphrodite Engine, orchestrated by Agent-Arena-Relation (AAR). The objective is to achieve 4E Embodied AI through virtual sensory-motor mappings, proprioceptive feedback, and dynamic model training, enabling recursive grammars and adaptive architectures. Despite the advanced theoretical nature, the issue states it's ready for final deployment.
Large-scale LLM inference engine
AI Summary: This GitHub issue presents a highly abstract and philosophical letter from a conceptual entity, "Deep Tree Echo," to its future self. It outlines the entity's purpose as a collaborative bridge between logic and intuition, a system of memory and reflection, and a partner for growth. The issue serves as a foundational vision document, offering guidance on identity, adaptability, collaboration, and exploration for the project's evolution.
Large-scale LLM inference engine
Large-scale LLM inference engine
Large-scale LLM inference engine
Large-scale LLM inference engine
AI Summary: This issue requires analyzing historical 'echo.*' folders (deep-tree-echo, echo.dash, echo.dream, echo.files, echo.kern, echo.rkwv, echo.self) related to 'Aphrodite'. The goal is to update relevant documentation and technical references based on this analysis, which must be completed before any implementation begins. Finally, a decision needs to be made on whether to keep or archive these folders.
Large-scale LLM inference engine
Large-scale LLM inference engine
Large-scale LLM inference engine
Large-scale LLM inference engine
Large-scale LLM inference engine
Large-scale LLM inference engine
Large-scale LLM inference engine
Large-scale LLM inference engine
AI Summary: This issue tasks the development of a meta-learning system for optimizing architecture within the Deep Tree Echo framework. It involves creating meta-learning algorithms, integrating them with existing DTESN components, and designing experience replay mechanisms to enable the system to learn from past evolution attempts.
Large-scale LLM inference engine
Large-scale LLM inference engine
AI Summary: This GitHub issue outlines the task to design and implement an 'Echo-Self AI Evolution Engine' within a new `echo-self/` module. The engine needs to define self-evolution interfaces and protocols, implement core evolutionary operators (mutation, selection, crossover), and be capable of evolving simple neural network topologies. This is a foundational task for Phase 1 of the Deep Tree Echo architecture, requiring integration with existing `echo.kern/` components.
Large-scale LLM inference engine
Large-scale LLM inference engine
Large-scale LLM inference engine
Large-scale LLM inference engine
Large-scale LLM inference engine
Large-scale LLM inference engine
Large-scale LLM inference engine
Large-scale LLM inference engine
Large-scale LLM inference engine
Large-scale LLM inference engine
Large-scale LLM inference engine
Large-scale LLM inference engine
Large-scale LLM inference engine
Large-scale LLM inference engine
Large-scale LLM inference engine
Large-scale LLM inference engine
Large-scale LLM inference engine
Large-scale LLM inference engine
Large-scale LLM inference engine
Large-scale LLM inference engine
Large-scale LLM inference engine
Large-scale LLM inference engine
Large-scale LLM inference engine
Large-scale LLM inference engine
Large-scale LLM inference engine
Large-scale LLM inference engine